Certifications & Compliance Framework - Big Hammer Security Guide

Executive Summary

In today’s regulatory landscape, enterprise AI platforms must demonstrate robust security, privacy, and compliance capabilities to earn client trust and meet regulatory requirements. Big Hammer’s comprehensive certification and compliance framework ensures we maintain the highest standards of security while protecting both our organization and our clients’ sensitive data and AI workloads.

This guide outlines our multi-layered approach across four critical security pillars:

Certifications & Compliance,

Infrastructure Security,

Data Security,

AI Security

Table of Contents

  1. Why Certifications & Compliance Matter
  2. Pillar 1: Certifications & Compliance
  3. Pillar 2: Infrastructure Security
  4. Pillar 3: Data Security
  5. Pillar 4: AI Security
  6. Implementation Roadmap
  7. Client Protection Benefits
  8. Continuous Improvement

Why Certifications & Compliance Matter

Business Imperatives

  • Enterprise Sales: Major clients require SOC 2, ISO 27001, and industry-specific certifications
  • Risk Mitigation: Reduce legal, financial, and reputational risks
  • Competitive Advantage: Differentiate in crowded AI marketplace
  • Global Market Access: Meet international regulatory requirements
  • Trust Building: Demonstrate commitment to security and privacy

Regulatory Landscape

  • Increasing AI Regulation: EU AI Act, NIST AI Risk Management Framework
  • Data Privacy Laws: GDPR, CCPA, state-level privacy regulations
  • Industry Standards: HIPAA for healthcare, FERPA for education, PCI DSS for payments
  • Government Requirements: FedRAMP for federal agencies, CMMC for defense contractors

Pillar 1: Certifications & Compliance

🎯 CASA Certified (In Progress)

What: Cloud Application Security Assessment - comprehensive security evaluation framework for cloud applications.

Why:

  • Validates cloud security architecture and controls
  • Required by many enterprise clients for cloud AI platforms
  • Demonstrates commitment to cloud security best practices

How We Implement:

  • Third-party security assessment and penetration testing
  • Continuous security monitoring and vulnerability management
  • Regular compliance audits and documentation updates
  • Cloud infrastructure hardening and configuration management

Current Status: To be audited and aligned with industry-standard security frameworks

🏆 SOC 2 Type 1 and Type 2 (Planned)

What: Service Organization Control reports that validate internal controls over security, availability, processing integrity, confidentiality, and privacy.

Why:

  • Type 1: Point-in-time assessment of control design
  • Type 2: Extended evaluation of control effectiveness over time
  • Essential for enterprise B2B sales and partnerships
  • Required by most Fortune 500 companies

How We Implement:

  • Security: Access controls, encryption, network security
  • Availability: System uptime, disaster recovery, incident response
  • Processing Integrity: Data accuracy, completeness, authorization
  • Confidentiality: Data classification, access restrictions, NDA compliance
  • Privacy: Data collection, use, retention, disposal practices

Implementation Steps:

  1. Gap analysis against SOC 2 Trust Principles
  2. Control design and documentation
  3. Third-party auditor selection and engagement
  4. 12-month observation period for Type 2
  5. Annual recertification and continuous monitoring

🌐 ISO 27001 (Planned)

What: International standard for information security management systems (ISMS).

Why:

  • Global recognition and acceptance
  • Systematic approach to managing sensitive information
  • Required for many international clients and government contracts
  • Foundation for other security certifications

How We Implement:

  • Risk Assessment: Identify, analyze, and evaluate information security risks
  • Control Selection: Implement appropriate security controls from ISO 27001 Annex A
  • ISMS Documentation: Policies, procedures, and work instructions
  • Management Review: Regular assessment and improvement of ISMS
  • Internal Audits: Ongoing compliance monitoring and gap identification
  • External Certification: Third-party assessment and certification

Key Control Areas (114 controls across 14 categories):

  • Information security policies
  • Organization of information security
  • Human resource security
  • Asset management
  • Access control
  • Cryptography
  • Physical and environmental security
  • Operations security
  • Communications security
  • System acquisition, development, and maintenance
  • Supplier relationships
  • Information security incident management
  • Business continuity management
  • Compliance

🏥 HIPAA Compliance (Industry Specific)

What: Health Insurance Portability and Accountability Act - protects Protected Health Information (PHI).

Why:

  • Required for healthcare AI applications
  • Severe penalties for violations ($100-$50,000 per record)
  • Builds trust with healthcare organizations
  • Enables expansion into healthcare verticals

How We Implement:

  • Administrative Safeguards:
    • HIPAA compliance officer appointment
    • Workforce training and access management
    • Business Associate Agreements (BAAs)
    • Incident response procedures
  • Physical Safeguards:
    • Facility access controls
    • Workstation security
    • Device and media controls
  • Technical Safeguards:
    • Access control and unique user identification
    • Audit controls and integrity protection
    • Transmission security and encryption

🇪🇺 GDPR Compliance (Global Requirement)

What: General Data Protection Regulation - EU privacy regulation with global impact.

Why:

  • Applies to any organization processing EU residents’ data
  • Fines up to 4% of global revenue or €20 million
  • Foundation for privacy-by-design principles
  • Required for European market access

How We Implement:

  • Lawful Basis: Clear legal justification for data processing
  • Data Minimization: Collect only necessary personal data
  • Purpose Limitation: Use data only for stated purposes
  • Data Subject Rights: Access, rectification, erasure, portability
  • Privacy by Design: Built-in privacy protection
  • Data Protection Impact Assessments (DPIAs): High-risk processing evaluation
  • Data Processing Records: Comprehensive documentation
  • Data Breach Notification: 72-hour reporting requirements

Pillar 2: Infrastructure Security

☁️ Cloud Servers Security

What: Multi-cloud infrastructure security across AWS and GCP platforms.

Implementation Details:

  • Provider Selection: AWS and GCP (both SOC 2, ISO 27001 certified)
  • Third-Party Audits: Regular security assessments by independent auditors
  • DDoS Protection: Cloud-native DDoS mitigation and rate limiting
  • Network Segmentation: Isolated environments for different security zones
  • Security Groups: Granular firewall rules and access controls
  • Vulnerability Management: Automated scanning and patch management

💾 Encrypted Backups

What: Geographically distributed, encrypted backup strategy for disaster recovery.

Implementation Details:

  • Primary Hosting: AWS U.S.-based regions for data sovereignty
  • Geographic Distribution: Multiple availability zones and regions
  • Encryption Standards: AES-256 encryption for all backup data
  • Retention Policies: Automated lifecycle management
  • Recovery Testing: Regular restore procedures and RTO/RPO validation
  • Compliance Alignment: Backup procedures meet regulatory requirements

🔐 Authentication, Authorization & Access Control

What: Comprehensive identity and access management (IAM) framework.

Implementation Components:

  • Single Sign-On (SSO): Centralized authentication across all systems
  • Multi-Factor Authentication (MFA): Required for all administrative access
  • Role-Based Access Control (RBAC): Principle of least privilege
  • Identity Providers: Integration with enterprise directory services
  • Session Management: Secure session handling and timeout policies
  • Privileged Access Management (PAM): Enhanced controls for administrative accounts
  • Access Reviews: Regular certification of user permissions
  • Audit Trails: Comprehensive logging of all access events

📊 Logging, Monitoring & Observability

What: Comprehensive visibility into system behavior, security events, and compliance activities.

Implementation Framework:

  • Real-Time Logging: All critical events and user interactions captured
  • Security Information and Event Management (SIEM): Centralized log analysis
  • Performance Monitoring: System health, usage patterns, and capacity planning
  • Anomaly Detection: Machine learning-based threat detection
  • AI Model Observability: Specialized monitoring for AI/ML workloads
  • Data Pipeline Monitoring: End-to-end data flow visibility
  • API Gateway Logging: Comprehensive API usage and security monitoring
  • Compliance Reporting: Automated generation of audit reports

⚡ High Availability (HA)

What: Resilient infrastructure design ensuring continuous service availability.

Implementation Strategy:

  • Auto-Scaling: Dynamic resource allocation based on demand
  • Health Checks: Continuous monitoring of service health
  • Failover Mechanisms: Automated switching to backup systems
  • Multi-AZ Deployment: Distribution across multiple availability zones
  • Load Balancing: Traffic distribution for optimal performance
  • Circuit Breakers: Graceful degradation during system stress
  • Disaster Recovery: Comprehensive business continuity planning

Pillar 3: Data Security

🚫 User Data Training Protection

What: Strict policies preventing user data from being used in AI model training.

Implementation Measures:

  • Opt-Out Policies: Explicit exclusion from LLM provider training datasets
  • Data Use Agreements: Contractual protections with AI providers
  • Data Isolation: Separate processing pipelines for user data
  • Audit Trails: Verification that user data is not used for training
  • Privacy-Preserving Techniques: Synthetic data generation for model improvement

🔒 Data Encryption In-Transit

What: End-to-end encryption for all data communications.

Technical Implementation:

  • TLS 1.3: Latest transport layer security protocols
  • Certificate Management: Automated certificate lifecycle management
  • Perfect Forward Secrecy: Protection against future key compromise
  • API Security: HTTPS enforcement for all API communications
  • Inter-Service Communication: mTLS for service-to-service communication

🛡️ Data Encryption At Rest

What: Comprehensive encryption for stored data across all systems.

Technical Specifications:

  • AES-256 Encryption: Industry-standard symmetric encryption
  • Key Management: Hardware Security Module (HSM) or cloud KMS
  • Database Encryption: Transparent Data Encryption (TDE)
  • File System Encryption: Full disk encryption for all storage
  • Backup Encryption: Encrypted backups with separate key management

📋 Data Classification & Sensitivity Management

What: Systematic categorization of data based on sensitivity and regulatory requirements.

Classification Levels:

  • Public: No restrictions on disclosure
  • Internal Use: Company confidential information
  • Confidential: Client data, business strategies
  • Restricted: PII, PHI, financial data, trade secrets

Implementation Controls:

  • Data Labeling: Automated classification and tagging
  • Access Controls: Permissions based on classification level
  • Handling Procedures: Specific protocols for each classification
  • Data Loss Prevention (DLP): Automated protection based on classification

⏰ Data Retention Policy

What: Comprehensive data lifecycle management ensuring compliance and efficiency.

Policy Framework:

  • Retention Schedules: Defined periods for different data types
  • Legal Holds: Suspension of deletion for litigation/investigation
  • Automated Deletion: Scheduled removal of expired data
  • Data Minimization: Collect and retain only necessary data
  • Cross-Border Considerations: Varying retention requirements by jurisdiction

🎭 Data Masking & PII Protection

What: Advanced techniques to protect personally identifiable information.

Technical Implementation:

  • Dynamic Data Masking: Real-time obfuscation for non-production environments
  • Static Data Masking: Permanent anonymization for development/testing
  • PII Detection: Machine learning-based identification of sensitive data
  • Tokenization: Replacement of sensitive data with non-sensitive tokens
  • Differential Privacy: Mathematical privacy guarantees

Pillar 4: AI Security

🛡️ Input Validation and Prompt Safety

What: Comprehensive protection against prompt injection and malicious inputs.

Implementation:

  • Input Sanitization: Removal of potentially harmful content
  • Prompt Injection Detection: Pattern recognition for adversarial inputs
  • Content Filtering: Allow/deny lists for acceptable input types
  • Rate Limiting: Prevention of abuse through usage controls
  • Validation Rules: Schema-based input validation

🎯 Output Handling

What: Filtering and moderation of AI model outputs to ensure safety and compliance.

Technical Controls:

  • Content Moderation: Automated detection of harmful content
  • Bias Detection: Identification and mitigation of biased outputs
  • Toxicity Filtering: Removal of offensive or inappropriate content
  • Fact-Checking: Validation of factual claims in outputs
  • Confidence Scoring: Quality assessment of generated content

⚖️ Model Behavior and Alignment

What: Ensuring AI models behave according to ethical guidelines and user intent.

Alignment Strategies:

  • Constitutional AI: Training models with explicit ethical principles
  • Human Feedback: Reinforcement learning from human preferences
  • Red Team Testing: Adversarial evaluation of model behavior
  • Bias Auditing: Regular assessment of model fairness
  • Value Alignment: Ensuring outputs reflect organizational values

📚 Training and Fine-Tuning Security

What: Secure practices for AI model development and improvement.

Security Measures:

  • Data Curation: Careful selection and validation of training data
  • Adversarial Training: Robustness against malicious inputs
  • Differential Privacy: Privacy-preserving training techniques
  • Model Auditing: Regular evaluation of model performance and bias
  • Version Control: Secure management of model versions and updates

🚦 Rate Limiting and Abuse Prevention

What: Controls to prevent misuse, overuse, and denial-of-service attacks.

Implementation:

  • API Rate Limiting: Request throttling per user/IP
  • Usage Quotas: Fair allocation of computational resources
  • Anomaly Detection: Identification of unusual usage patterns
  • Cost Controls: Prevention of excessive resource consumption
  • Abuse Monitoring: Detection of malicious or inappropriate usage

🔌 Plugin and Tooling Security

What: Secure integration of third-party tools and extensions.

Security Framework:

  • Plugin Validation: Security assessment of third-party integrations
  • Sandboxing: Isolated execution environments for plugins
  • Permission Management: Granular control over plugin capabilities
  • API Security: Secure interfaces for plugin communication
  • Audit Logging: Comprehensive tracking of plugin activities

📦 Supply Chain and Dependency Management

What: Security monitoring of third-party software and dependencies.

Management Practices:

  • Vulnerability Scanning: Automated detection of known vulnerabilities
  • Dependency Tracking: Comprehensive inventory of all dependencies
  • License Compliance: Ensuring legal compliance of third-party software
  • Update Management: Secure and timely application of security patches
  • Vendor Assessment: Security evaluation of software suppliers

🏰 Environment and Execution Isolation

What: Containerized and sandboxed execution environments for AI models.

Isolation Techniques:

  • Container Security: Docker/Kubernetes with security hardening
  • Network Segmentation: Isolated networks for different components
  • Resource Limits: Prevention of resource exhaustion attacks
  • Privilege Restriction: Minimal permissions for running processes
  • Data Isolation: Separation of client data and model execution

🔒 Data Privacy and Leakage Prevention

What: Protection of user data from memorization or exposure by AI models.

Privacy Controls:

  • Data Anonymization: Removal of identifying information
  • Model Unlearning: Techniques to remove specific data from models
  • Differential Privacy: Mathematical privacy guarantees
  • Output Filtering: Detection and removal of leaked information
  • Privacy Auditing: Regular assessment of privacy risks

👤 Authentication and Authorization for AI

What: Identity verification and access control specifically for AI model usage.

AI-Specific Controls:

  • Model Access Controls: Permissions for different AI capabilities
  • User Authentication: Secure identity verification for AI services
  • API Key Management: Secure distribution and rotation of API keys
  • Usage Attribution: Tracking of AI usage by user and application
  • Compliance Integration: Alignment with broader IAM policies

📈 Monitoring and Logging for AI

What: Specialized monitoring for AI systems, including model performance and usage.

AI Monitoring Framework:

  • Model Performance: Tracking accuracy, latency, and drift
  • Usage Analytics: Understanding patterns of AI system usage
  • Anomaly Detection: Identification of unusual model behavior
  • Security Events: Logging of potential security incidents
  • Compliance Reporting: Automated generation of regulatory reports

🏛️ IP Protection and Model Security

What: Protection of intellectual property, including model weights and proprietary algorithms.

IP Protection Measures:

  • Model Encryption: Protection of model files and weights
  • Access Controls: Restricted access to proprietary algorithms
  • Watermarking: Techniques to identify unauthorized model usage
  • API Security: Secure interfaces that don’t expose model details
  • Legal Protections: Contracts and NDAs for model access

✅ Compliance and Audit for AI

What: Ensuring AI systems meet legal, ethical, and regulatory standards.

Compliance Framework:

  • Regulatory Mapping: Alignment with AI-specific regulations
  • Ethical Guidelines: Implementation of AI ethics principles
  • Audit Trails: Comprehensive logging for compliance verification
  • Risk Assessments: Regular evaluation of AI-related risks
  • Documentation: Detailed records of AI system design and operation

Implementation Roadmap

Phase 1: Foundation

Priority: Establish core compliance framework

  • Complete GDPR compliance implementation
  • Begin SOC 2 Type 1 preparation
  • Implement basic data classification system
  • Deploy comprehensive logging and monitoring
  • Establish incident response procedures

Phase 2: Certification

Priority: Achieve key certifications

  • Complete SOC 2 Type 1 audit
  • Begin ISO 27001 implementation
  • Start CASA certification process
  • Implement advanced AI security controls
  • Deploy automated compliance monitoring

Phase 3: Advanced Compliance

Priority: Industry-specific compliance

  • Complete SOC 2 Type 2 (12-month observation)
  • Achieve ISO 27001 certification
  • Implement HIPAA compliance framework
  • Deploy advanced threat detection
  • Complete CASA certification

Phase 4: Continuous Improvement

Priority: Maintain and enhance compliance posture

  • Annual recertification processes
  • Continuous security monitoring
  • Regular compliance audits
  • Threat intelligence integration
  • Emerging regulation adaptation

Client Protection Benefits

Enterprise Clients

  • Reduced Risk: Demonstrated compliance reduces client risk exposure
  • Procurement Simplification: Certifications streamline vendor approval
  • Regulatory Confidence: Assurance of meeting industry requirements
  • Data Protection: Multi-layered security for sensitive client data

Healthcare Organizations

  • HIPAA Compliance: Ready-to-use PHI protection framework
  • Medical AI Safety: Specialized controls for healthcare AI applications
  • Audit Support: Comprehensive documentation for regulatory audits
  • Risk Management: Proactive identification and mitigation of healthcare-specific risks

Financial Services

  • Regulatory Alignment: Support for FFIEC, PCI DSS, and other financial regulations
  • Data Residency: Control over data location for regulatory compliance
  • Fraud Detection: AI security controls adapted for financial use cases
  • Audit Trails: Comprehensive logging for regulatory examination

Government Agencies

  • Security Clearance: Framework for classified data handling
  • FISMA Compliance: Federal information security management alignment
  • Authority to Operate (ATO): Support for government certification processes
  • Supply Chain Security: Enhanced vendor risk management

Continuous Improvement

Regular Assessments

  • Quarterly Security Reviews: Internal assessment of security posture
  • Annual Penetration Testing: Third-party security validation
  • Compliance Audits: Regular evaluation of regulatory alignment
  • Risk Assessments: Ongoing identification and mitigation of new risks

Threat Intelligence

  • Industry Monitoring: Tracking of emerging threats and vulnerabilities
  • Regulatory Updates: Monitoring of changing compliance requirements
  • Best Practice Evolution: Adoption of evolving security standards
  • Peer Benchmarking: Comparison with industry security practices

Technology Evolution

  • Security Tool Updates: Regular enhancement of security technologies
  • AI Security Research: Integration of latest AI security developments
  • Automation Enhancement: Continuous improvement of automated controls
  • Integration Optimization: Better integration between security tools and processes

Training and Awareness

  • Security Training: Regular education for all staff members
  • Compliance Updates: Training on new regulatory requirements
  • Incident Response Drills: Regular testing of response procedures
  • Third-Party Education: Training for vendors and partners

Conclusion

Big Hammer’s comprehensive certification and compliance framework provides robust protection for both our organization and our clients. Through systematic implementation of industry-leading security controls, privacy protections, and regulatory compliance measures, we create a trustworthy foundation for enterprise AI applications.

Our multi-pillar approach ensures that security is not an afterthought but a fundamental aspect of our platform design and operation. This framework not only protects against current threats but also provides the flexibility to adapt to emerging risks and evolving regulatory requirements.

By achieving and maintaining these certifications and compliance standards, Big Hammer demonstrates our commitment to security excellence and provides our clients with the confidence they need to deploy AI solutions at enterprise scale.

This framework positions Big Hammer as a security-first AI platform provider, enabling us to serve the most demanding enterprise clients while maintaining the highest standards of data protection and regulatory compliance.